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Articoli di riviste sul tema "Reservoir inflow forecasting"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 11 febbraio 2022

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1

Uang-Aree, Prawit, and Sununtha Kingpaiboon. "Possibility of GPS precipitable water vapour for reservoir inflow forecasting." Journal of Water and Land Development 36, no. 1 (2018): 161–71. http://dx.doi.org/10.2478/jwld-2018-0016.

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AbstractWe investigated the possibility of using GPS precipitable water vapour (GPS-PWV) for forecasting reservoir inflow. The correlations between monthly GPS-PWV and the inflow of two reservoirs were examined and the relationship tested, using a group method of data handling (GMDH) type neural network algorithm. The daily and monthly reservoir inflows were directly proportional to daily and monthly GPS-PWV trends. Peak reservoir inflow, however, shifted from the peak averages for GPS-PWV. A strong relationship between GPS-PWV and inflow was confirmed by high R2 values, high coefficients of c

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2

Chiamsathit, Chuthamat, Adebayo J. Adeloye, and Soundharajan Bankaru-Swamy. "Inflow forecasting using Artificial Neural Networks for reservoir operation." Proceedings of the International Association of Hydrological Sciences 373 (May 12, 2016): 209–14. http://dx.doi.org/10.5194/piahs-373-209-2016.

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Abstract. In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is u

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3

Yang, Sheng-Chi, and Tsun-Hua Yang. "Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS." Advances in Meteorology 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/581756.

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During an extreme event, having accurate inflow forecasting with enough lead time helps reservoir operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate reservoirs could help maximize flood protection while saving water for drier times of the year. This study combines ensemble quantitative precipitation forecasts and a hydrological model to provide a 3-day reservoir inflow in the Shihmen Reservoir, Taiwan. A total of six historical typhoons were used for model calibration, validation, and application. An understanding of cascaded uncertainties from t

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4

Zhong, Yixuan, Shenglian Guo, Huanhuan Ba, Feng Xiong, Fi-John Chang, and Kairong Lin. "Evaluation of the BMA probabilistic inflow forecasts using TIGGE numeric precipitation predictions based on artificial neural network." Hydrology Research 49, no. 5 (2018): 1417–33. http://dx.doi.org/10.2166/nh.2018.177.

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Abstract Reservoir inflow forecasting is a crucial task for reservoir management. Without considering precipitation predictions, the lead time for inflow is subject to the concentration time of precipitation in the basin. With the development of numeric weather prediction (NWP) techniques, it is possible to forecast inflows with long lead times. Since larger uncertainty usually occurs during the forecasting process, much attention has been paid to probabilistic forecasts, which uses a probabilistic distribution function instead of a deterministic value to predict the future status. In this stu

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5

Ngamsanroaj, Yaowalak, and Kreangsak Tamee. "Improving model using estimate error for daily inflow forecasting." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, no. 2 (2020): 170–77. http://dx.doi.org/10.37936/ecti-cit.2019132.198508.

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Inflow forecasting is one of the important components for reservoir operation and resource management. To obtain enhanced accuracy for forecasting reservoir inflow, this paper proposed an improved model for forecasting the inflow of Bhumibol reservoir. The 3,169 records of daily inflow data from June 1, 2008, to February 1, 2017, had been collected to calculate the inflow into the reservoir by using Artificial Neural Networks (ANN) Back-Propagation Learning Algorithm for forecasting the inflow of the reservoir in the main model and error prediction model. The performance of the model can be ev

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6

Xu, Shichao, Yangbo Chen, Lixue Xing, and Chuan Li. "Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model." Water 13, no. 3 (2021): 272. http://dx.doi.org/10.3390/w13030272.

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For reservoir basins, complex underlying surface conditions, short flood confluence times, and concentrated water volumes make inflow flood forecasting difficult and cause forecast accuracies to be low. Conventional flood forecasting models can no longer meet the required forecast accuracy values for flood control operations. To give full play to the role of reservoirs in flood control and to maximize the use of reservoir flood resources, high-precision inflow flood forecasting is urgently needed as a support mechanism. In this study, the Baipenzhu Reservoir in Guangdong Province was selected

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7

Kang, Jaewon. "Probabilistic Forecasting of Seasonal Inflow to Reservoir." Journal of the Environmental Sciences international 22, no. 8 (2013): 965–77. http://dx.doi.org/10.5322/jesi.2013.22.8.965.

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8

Kale, M. U., M. B. Nagdeve, and S. B. Wadatkar. "Reservoir Inflow Forecasting using Artificial Neural Network." Hydrology Journal 35, no. 1and2 (2012): 52. http://dx.doi.org/10.5958/j.0971-569x.35.1x.005.

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9

Banihabib, Mohammad Ebrahim, Reihaneh Bandari, and Mohammad Valipour. "Improving Daily Peak Flow Forecasts Using Hybrid Fourier-Series Autoregressive Integrated Moving Average and Recurrent Artificial Neural Network Models." AI 1, no. 2 (2020): 263–75. http://dx.doi.org/10.3390/ai1020017.

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In multi-purpose reservoirs, to achieve optimal operation, sophisticated models are required to forecast reservoir inflow in both short- and long-horizon times with an acceptable accuracy, particularly for peak flows. In this study, an auto-regressive hybrid model is proposed for long-horizon forecasting of daily reservoir inflow. The model is examined for a one-year horizon forecasting of high-oscillated daily flow time series. First, a Fourier-Series Filtered Autoregressive Integrated Moving Average (FSF-ARIMA) model is applied to forecast linear behavior of daily flow time series. Second, a

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10

Lee, Donghee, Hwansuk Kim, Ilwon Jung, and Jaeyoung Yoon. "Monthly Reservoir Inflow Forecasting for Dry Period Using Teleconnection Indices: A Statistical Ensemble Approach." Applied Sciences 10, no. 10 (2020): 3470. http://dx.doi.org/10.3390/app10103470.

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Reliable long-range reservoir inflow forecast is essential to successfully manage water supply from reservoirs. This study aims to develop statistical reservoir inflow forecast models for a reservoir watershed, based on hydroclimatic teleconnection between monthly reservoir inflow and climatic variables. Predictability of such a direct relationship has not been assessed yet at the monthly time scale using the statistical ensemble approach that employs multiple data-driven models as an ensemble. For this purpose, three popular data-driven models, namely multiple linear regression (MLR), support

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11

Agrawal, Niraj Kumar, Anil Kumar Lohani, and N. K. Goel. "Physiographic Analysis of Tehri Dam Catchment and Development of GIUH Based Nash Model for Ungauged Rivers." Current World Environment 14, no. 2 (2019): 215–30. http://dx.doi.org/10.12944/cwe.14.2.06.

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Advanced information about incoming flows is required for operation of a variety of hydraulic structures including multipurpose storage hydropower projects. Inflow forecasts are used for optimum power generation during non -monsoon season and operation of gates and spillways during the flood season. In order to develop an inflow forecasting system for a reservoir, it has been observed that many a times number of ungauged rivers directly falling into the reservoirs are not accounted for. Such is the case for the Tehri Reservoir, where 16 small rivers/tributaries which are directly contributing

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12

Bai, Yun, Jingjing Xie, Xiaoxue Wang, and Chuan Li. "Model fusion approach for monthly reservoir inflow forecasting." Journal of Hydroinformatics 18, no. 4 (2016): 634–50. http://dx.doi.org/10.2166/hydro.2016.141.

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Considering the complexity of reservoir systems, a model fusion approach is proposed in this paper. According to different inflow information represented, the historical monthly data can be constructed as two time series, namely, yearly-scale series and monthly-scale series. Even grey model (EGM) and adaptive neuro-fuzzy inference system (ANFIS) are adopted for the forecasts at the two scales, respectively. Grey relational analysis (GRA) is subsequently used as a scale-normalized model fusion tool to integrate the two scales' results. The proposed method is evaluated using the data of the Thre

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13

Coulibaly, Paulin, François Anctil, and Bernard Bobée. "Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks." Journal of Hydrologic Engineering 6, no. 5 (2001): 367–76. http://dx.doi.org/10.1061/(asce)1084-0699(2001)6:5(367).

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Rosyadi, Imron. "Peramalan Aliran Masukan Waduk Mrica Menggunakan ModelThomas-Fieringdan Jaringan Syaraf Tiruan ANFIS." Dinamika Rekayasa 7, no. 2 (2011): 48. http://dx.doi.org/10.20884/1.dr.2011.7.2.49.

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<p>Inflow forecasting in hydrology processes is important tool in water resources management,planning, and utilization. The fulfillment of this operational hydrology isvery applicable, especially where onlyan insufficient amount of data collected over an insufficient length of time is available. The Thomas-Fiering Method is one of the most useful and widely used synthetic flow models. In last year’s, ArtificialNeural Network (ANN)method and Fuzzy Logic have introduced in hydrological processes. Mrica hydropower reservoir in Central Java, Indonesia, has suffered water sustainability anden

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15

Banihabib, Mohammad Ebrahim, Arezoo Ahmadian, and Mohammad Valipour. "Hybrid MARMA-NARX model for flow forecasting based on the large-scale climate signals, sea-surface temperatures, and rainfall." Hydrology Research 49, no. 6 (2018): 1788–803. http://dx.doi.org/10.2166/nh.2018.145.

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Abstract In this study, to reflect the effect of large-scale climate signals on runoff, these indices are accompanied with rainfall (the most effective local factor in runoff) as the inputs of the hybrid model. Where one-year in advance forecasting of reservoir inflows can provide data to have an optimal reservoir operation, reports show we still need more accurate models which include all effective parameters to have more forecasting accuracy than traditional linear models (ARMA and ARIMA). Thus, hybridization of models was employed for improving the accuracy of flow forecasting. Moreover, va

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16

Tsao, Hao-Han, Yih-Guang Leu, Li-Fen Chou, and Chao-Yang Tsao. "A Method of Multi-Stage Reservoir Water Level Forecasting Systems: A Case Study of Techi Hydropower in Taiwan." Energies 14, no. 12 (2021): 3461. http://dx.doi.org/10.3390/en14123461.

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Reservoirs in Taiwan often provide hydroelectric power, irrigation water, municipal water, and flood control for the whole year. Taiwan has the climatic characteristics of concentrated rainy seasons, instantaneous heavy rains due to typhoons and rainy seasons. In addition, steep rivers in mountainous areas flow fast and furiously. Under such circumstances, reservoirs have to face sudden heavy rainfall and surges in water levels within a short period of time, which often causes the water level to continue to rise to the full level even though hydroelectric units are operating at full capacity,

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17

Amnatsan, Somchit, Sayaka Yoshikawa, and Shinjiro Kanae. "Improved Forecasting of Extreme Monthly Reservoir Inflow Using an Analogue-Based Forecasting Method: A Case Study of the Sirikit Dam in Thailand." Water 10, no. 11 (2018): 1614. http://dx.doi.org/10.3390/w10111614.

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Reservoir inflow forecasting is crucial for appropriate reservoir management, especially in the flood season. Forecasting for this season must be sufficiently accurate and timely to allow dam managers to release water gradually for flood control in downstream areas. Recently, several models and methodologies have been developed and applied for inflow forecasting, with good results. Nevertheless, most were reported to have weaknesses in capturing the peak flow, especially rare extreme flows. In this study, an analogue-based forecasting method, designated the variation analogue method (VAM), was

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18

Li, Chuan, Yun Bai, and Bo Zeng. "Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting." Water Resources Management 30, no. 14 (2016): 5145–61. http://dx.doi.org/10.1007/s11269-016-1474-8.

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19

Apaydin, Halit, Hajar Feizi, Mohammad Taghi Sattari, Muslume Sevba Colak, Shahaboddin Shamshirband, and Kwok-Wing Chau. "Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting." Water 12, no. 5 (2020): 1500. http://dx.doi.org/10.3390/w12051500.

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Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For

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20

Alquraish, Mohammed M., Khaled A. Abuhasel, Abdulrahman S. Alqahtani, and Mosaad Khadr. "A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia)." Water 13, no. 9 (2021): 1236. http://dx.doi.org/10.3390/w13091236.

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The precise prediction of the streamflow of reservoirs is of considerable importance for many activities relating to water resource management, such as reservoir operation and flood and drought control and protection. This study aimed to develop and evaluate the applicability of a hidden Markov model (HMM) and two hybrid models, i.e., the support vector machine-genetic algorithm (SVM-GA) and artificial neural fuzzy inference system-genetic algorithm (ANFIS-GA), for reservoir inflow forecasting at the King Fahd dam, Saudi Arabia. The results obtained by the HMM model were compared with those fo

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21

Chen, Yu-Wen, Jui-Pin Tsai, Liang-Cheng Chang, Chih-Chao Ho, and You-Cheng Chen. "The development of a real-time flooding operation model in the Tseng-Wen Reservoir." Hydrology Research 45, no. 3 (2013): 490–503. http://dx.doi.org/10.2166/nh.2013.301.

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Typhoon events occur frequently in Taiwan resulting in flood-related disasters. A well-operated reservoir can reduce the severity of a disaster. This study incorporates a genetic algorithm, a river hydraulic model, an artificial neural network and a simulation model of Tseng-Wen Reservoir to propose a real-time flooding operation model. The model includes two parts: an optimal flooding operation model (OFOM) and a reservoir inflow forecasting. Given an inflow condition, the OFOM is run based on the safety of the dam structure, reservoir flooding operation rule, and minimization of the downstre

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22

Gragne, A. S., A. Sharma, R. Mehrotra, and K. Alfredsen. "Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework." Hydrology and Earth System Sciences Discussions 11, no. 10 (2014): 12063–101. http://dx.doi.org/10.5194/hessd-11-12063-2014.

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Abstract. Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independen

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23

Kang, Jaewon. "Forecasting of Seasonal Inflow to Reservoir Using Multiple Linear Regression." Journal of the Environmental Sciences international 22, no. 8 (2013): 953–63. http://dx.doi.org/10.5322/jesi.2013.22.8.953.

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Yu, Myungsu, Youngmok Lee, and Jaeeung Yi. "Flood inflow forecasting on HantanRiver reservoir by using forecasted rainfall." Journal of Korea Water Resources Association 49, no. 4 (2016): 327–33. http://dx.doi.org/10.3741/jkwra.2016.49.4.327.

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Bourdin, Dominique R., Thomas N. Nipen, and Roland B. Stull. "Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system." Water Resources Research 50, no. 4 (2014): 3108–30. http://dx.doi.org/10.1002/2014wr015462.

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Crochemore, Louise, Maria-Helena Ramos, Florian Pappenberger, Schalk Jan van Andel, and Andrew W. Wood. "An Experiment on Risk-Based Decision-Making in Water Management Using Monthly Probabilistic Forecasts." Bulletin of the American Meteorological Society 97, no. 4 (2016): 541–51. http://dx.doi.org/10.1175/bams-d-14-00270.1.

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Abstract The use of probabilistic forecasts is necessary to take into account uncertainties and allow for optimal risk-based decisions in streamflow forecasting at monthly to seasonal lead times. Such probabilistic forecasts have long been used by practitioners in the operation of water reservoirs, in water allocation and management, and more recently in drought preparedness activities. Various studies assert the potential value of hydrometeorological forecasting efforts, but few investigate how these forecasts are used in the decision-making process. Role-playing games can help scientists, ma

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Gragne, A. S., A. Sharma, R. Mehrotra, and K. Alfredsen. "Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework." Hydrology and Earth System Sciences 19, no. 8 (2015): 3695–714. http://dx.doi.org/10.5194/hess-19-3695-2015.

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Abstract. Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be

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Ye, Yan, Guang Wen Ma, and Yuan Bo Zhang. "The Advances in Risk Analysis of Reservoir Operation Research." Advanced Materials Research 594-597 (November 2012): 1993–97. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.1993.

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There are many uncertain factors in the process of reservoir’s operation. For the reasons, the "deterministic theory" must turn to" probability theory", and it becomes necessary to strengthen risk analysis. Summarize the connotation of reservoir risk analysis, progress in research of risk analysis for reservoir inflow forecasting, risk analysis for reservoir operation, risk analysis for flood protection and flood discharge , and reservoir multi-objective risk analysis. There are so much influence factors and much of them uncertainly, not enough research on multiple targets and multiple factors

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Michailovsky, C. I., and P. Bauer-Gottwein. "Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study." Hydrology and Earth System Sciences 18, no. 3 (2014): 997–1007. http://dx.doi.org/10.5194/hess-18-997-2014.

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Abstract. River basin management can greatly benefit from short-term river discharge predictions. In order to improve model produced discharge forecasts, data assimilation allows for the integration of current observations of the hydrological system to produce improved forecasts and reduce prediction uncertainty. Data assimilation is widely used in operational applications to update hydrological models with in situ discharge or level measurements. In areas where timely access to in situ data is not possible, remote sensing data products can be used in assimilation schemes. While river discharg

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Michailovsky, C. I., and P. Bauer-Gottwein. "Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study." Hydrology and Earth System Sciences Discussions 10, no. 7 (2013): 9615–44. http://dx.doi.org/10.5194/hessd-10-9615-2013.

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Abstract. River basin management can greatly benefit from short-term river discharge predictions. In order to improve model produced discharge forecasts, data assimilation allows for the integration of current observations of the hydrological system to produce optimal forecasts and reduce prediction uncertainty. Data assimilation is widely used in operational applications to update hydrological models with in situ discharge or level measurements. In areas where timely access to in situ data is not possible, remote sensing data products can be used in assimilation schemes. While river discharge

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Nayak, P. C., and K. P. Sudheer. "Fuzzy model identification based on cluster estimation for reservoir inflow forecasting." Hydrological Processes 22, no. 6 (2008): 827–41. http://dx.doi.org/10.1002/hyp.6644.

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Bourdin, Dominique R., Thomas N. Nipen, and Roland B. Stull. "Retracted: Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system." Water Resources Research 50, no. 2 (2014): n/a. http://dx.doi.org/10.1002/2013wr014228.

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Lin, Gwo-Fong, Guo-Rong Chen, and Pei-Yu Huang. "Effective typhoon characteristics and their effects on hourly reservoir inflow forecasting." Advances in Water Resources 33, no. 8 (2010): 887–98. http://dx.doi.org/10.1016/j.advwatres.2010.04.016.

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Taghi Sattari, M., Kadri Yurekli, and Mahesh Pal. "Performance evaluation of artificial neural network approaches in forecasting reservoir inflow." Applied Mathematical Modelling 36, no. 6 (2012): 2649–57. http://dx.doi.org/10.1016/j.apm.2011.09.048.

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Ma, Qiumei, Jiaxin Zhang, Bin Xiong, Yanke Zhang, Changming Ji, and Ting Zhou. "Quantifying the Risks that Propagate from the Inflow Forecast Uncertainty to the Reservoir Operations with Coupled Flood and Electricity Curtailment Risks." Water 13, no. 2 (2021): 173. http://dx.doi.org/10.3390/w13020173.

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Substantial uncertainty is inherent in reservoir inflow forecasting, which exerts a potential negative impact on reservoir risk. However, the risk propagation from the inflow forecast uncertainty (IFU) to reservoir operations remains elusive. Thus, a new integrated assessment framework was developed in this study to characterize the risk coupling with flood and electricity curtailment risks that propagate from the IFU to the reservoir operations. First, to incorporate the IFU, an improved Gaussian mixture distribution (IGMD) and Markov chain Monte Carlo (MCMC) algorithm were constructed to mod

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Xu, Wei, Xiaoying Fu, Xia Li, and Ming Wang. "Data transformation models utilized in Bayesian probabilistic forecast considering inflow forecasts." Hydrology Research 50, no. 5 (2019): 1267–80. http://dx.doi.org/10.2166/nh.2019.028.

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Abstract This paper presents a new Bayesian probabilistic forecast (BPF) model to improve the efficiency and reliability of normal distribution transformation and to describe the uncertainties of medium-range forecasting inflows with 10 days forecast horizons. In this model, the inflow data will be transformed twice to a standard normal distribution. The Box–Cox (BC) model is first used to quickly transform the inflow data with a normal distribution, and then, the transformed data are converted to a standard normal distribution by the meta-Gaussian (MG) model. Based on the transformed inflows

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Gelfan, Alexander, Vsevolod Moreydo, Yury Motovilov, and Dimitri P. Solomatine. "Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios." Hydrology and Earth System Sciences 22, no. 4 (2018): 2073–89. http://dx.doi.org/10.5194/hess-22-2073-2018.

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Abstract. A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia), is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics) that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast), and synthetic weather data, simulated by a multi-site weather generator (W

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Lin, Gwo-Fong, Tsung-Chun Wang, and Lu-Hsien Chen. "A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods." Advances in Meteorology 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/7575126.

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This study describes the development of a reservoir inflow forecasting model for typhoon events to improve short lead-time flood forecasting performance. To strengthen the forecasting ability of the original support vector machines (SVMs) model, the self-organizing map (SOM) is adopted to group inputs into different clusters in advance of the proposed SOM-SVM model. Two different input methods are proposed for the SVM-based forecasting method, namely, SOM-SVM1 and SOM-SVM2. The methods are applied to an actual reservoir watershed to determine the 1 to 3 h ahead inflow forecasts. For 1, 2, and

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Budu, Krishna. "Comparison of Wavelet-Based ANN and Regression Models for Reservoir Inflow Forecasting." Journal of Hydrologic Engineering 19, no. 7 (2014): 1385–400. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000892.

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Coulibaly, P., F. Anctil, and B. Bobée. "Daily reservoir inflow forecasting using artificial neural networks with stopped training approach." Journal of Hydrology 230, no. 3-4 (2000): 244–57. http://dx.doi.org/10.1016/s0022-1694(00)00214-6.

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Suhili, Rafa H. Al, and Rizgar A. Karim. "Daily inflow forecasting for Dukan reservoir in Iraq using artificial neural networks." International Journal of Water 9, no. 2 (2015): 194. http://dx.doi.org/10.1504/ijw.2015.068961.

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Karimi-Goo, S. H., and T. S. Lee. "Applicability of Adaptive Neuro-Fuzzy Inference Systems in Daily Reservoir Inflow Forecasting." International Journal of Soft Computing 6, no. 3 (2011): 75–84. http://dx.doi.org/10.3923/ijscomp.2011.75.84.

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43

Bai, Yun, Zhiqiang Chen, Jingjing Xie, and Chuan Li. "Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models." Journal of Hydrology 532 (January 2016): 193–206. http://dx.doi.org/10.1016/j.jhydrol.2015.11.011.

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Yan, Baowei, Shenglian Guo, and Lu Chen. "Estimation of reservoir flood control operation risks with considering inflow forecasting errors." Stochastic Environmental Research and Risk Assessment 28, no. 2 (2013): 359–68. http://dx.doi.org/10.1007/s00477-013-0756-4.

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45

Renaningsih, Saraswati, Rachmad Jayadi, and Istiarto Istiarto. "Integrated Simulation of Spillway and Diversion Structure in Flood Risk Assessment of Upper Solo River." Journal of the Civil Engineering Forum 4, no. 2 (2018): 127. http://dx.doi.org/10.22146/jcef.34166.

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Abstract (sommario):

Wonogiri Reservoir is a multi-purpose hydraulic structure that is used for water supply service and flood control in the watershed area of Upper Solo River. Inflow of the Wonogiri Reservoir comes from 10 rivers in the watershed’s catchment area. As for the purpose of reservoir’s sediment controller, a new spillway is constructed; and also closure dike is being built, which influenced the change on reservoir storage characteristic, and then means that a new reservoir operation guideline is necessary. Therefore, study on flood routing on the watershed of Upper Solo River at the new condition of

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46

Liu, Xiaoli, and Paulin Coulibaly. "Downscaling Ensemble Weather Predictions for Improved Week-2 Hydrologic Forecasting." Journal of Hydrometeorology 12, no. 6 (2011): 1564–80. http://dx.doi.org/10.1175/2011jhm1366.1.

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Abstract (sommario):

Abstract This study investigates the use of large-scale ensemble weather predictions provided by the National Centers for Environmental Prediction (NCEP) Global Forecast System [GFS; formerly known as Medium-Range Forecast (MRF)] for improving week-2 hydrologic forecasting. The ensemble weather predictor variables are used to downscale daily precipitation and temperature series at two meteorological stations in the Saguenay watershed in northeastern Canada. Three data-driven methods—namely, the statistical downscaling model (SDSM), the time-lagged feed-forward neural network (TLFN), and evolut

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47

Santos, C. A. G., P. K. M. M. Freire, G. B. L. Silva, and R. M. Silva. "Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir." Proceedings of the International Association of Hydrological Sciences 364 (September 16, 2014): 100–105. http://dx.doi.org/10.5194/piahs-364-100-2014.

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Abstract. This paper proposes the use of discrete wavelet transform (DWT) to remove the high-frequency components (details) of an original signal, because the noises generally present in time series (e.g. streamflow records) may influence the prediction quality. Cleaner signals could then be used as inputs to an artificial neural network (ANN) in order to improve the model performance of daily discharge forecasting. Wavelet analysis provides useful decompositions of original time series in high and low frequency components. The present application uses the Coiflet wavelets to decompose hydrolo

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Cheng, Chun-Tian, Zhong-Kai Feng, Wen-Jing Niu, and Sheng-Li Liao. "Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China." Water 7, no. 12 (2015): 4477–95. http://dx.doi.org/10.3390/w7084477.

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Yu, Yang, Peifang Wang, Chao Wang, Jin Qian, and Jun Hou. "Combined Monthly Inflow Forecasting and Multiobjective Ecological Reservoir Operations Model: Case Study of the Three Gorges Reservoir." Journal of Water Resources Planning and Management 143, no. 8 (2017): 05017004. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000786.

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Hu, Hu, Kan Yang, and Zhe Yang. "Adaptive Reservoir Inflow Forecasting Using Variational Mode Decomposition and Long Short-Term Memory." IEEE Access 9 (2021): 119032–48. http://dx.doi.org/10.1109/access.2021.3107502.

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